This paper reveals how the music and soundscapes used in toy commercials are reinforcing rigid gender norms. This research is particularly relevant given the ongoing conversation about gender representation in media and the increasing awareness of the impact of advertising on children. This is the first study to comprehensively examine this aspect of toy advertising and its implications for gender perceptions. 

Key findings from the study include:

Distinct musical styles for boys and girls: Commercials aimed at boys tend to feature louder, more abrasive music, while those aimed at girls use softer, more harmonious sounds.

Emotional impact of music: The music used in toy commercials reinforces gender stereotypes through its emotional impact, shaping children's affective experiences.

Societal implications: Gendered music in advertising can influence children's career aspirations, body image concerns, and even mental health.

Historical and cultural roots: The gendered associations between certain instruments and gender identities have deep historical and cultural roots.

Music-primed gender schemas: Music can evoke gendered meanings and expectations, shaping children's perceptions of what is "appropriate" for boys and girls.

The study's findings have significant implications for policymakers, advertisers, and parents. It highlights the need for more comprehensive regulations to address harmful gender stereotypes in advertising and for greater awareness of the subtle ways in which media can shape children's understanding of gender.

For parents of children with disabilities, finding time to focus on themselves may be difficult. However, a new study finds that the right coping strategies and resilience can significantly help manage the challenges of raising children with special needs.

AI-powered apps offering medical diagnoses at the click of a button are often limited by biased data and a lack of regulation, leading to inaccurate and unsafe health advice, a new study found.

McGill University researchers presented symptom data from known medical cases to two popular, representative apps to see how well they diagnosed the conditions. While the apps sometimes gave correct diagnoses, they often failed to detect serious conditions, according to findings published in the Journal of Medical Internet Research. This potentially resulted in delayed treatment.